(2024) 22:319
Wang et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03399-8

BMC Medicine

RESEARCH ARTICLE

Open AccessHistory of childhood maltreatment
associated with hospitalization or death due
to COVID‑19: a cohort study
Yue Wang1,2†, Fenfen Ge1,2†, Thor Aspelund1, Helga Ask3,4, Arna Hauksdóttir1, Kejia Hu2, Jóhanna Jakobsdóttir1,
Helga Zoega1,5, Qing Shen6,7, Heather C. Whalley8,9, Ole Birger Vesterager Pedersen10,11, Kelli Lehto12,
Ole A. Andreassen13,14, Fang Fang2, Huan Song1,15,16 and Unnur A. Valdimarsdóttir1,2,17*   

Abstract
Background Childhood maltreatment (CM) has been indicated in adverse health outcomes across the lifespan,
including severe infection-related outcomes. Yet, data are scarce on the potential role of CM in severe COVID-19-related outcomes as well as on mechanisms underlying this association.
Methods We included 151,427 individuals in the UK Biobank who responded to questions on the history of CM
in 2016 and 2017 and were alive on January 31, 2020. Binomial logistic regression models were performed to estimate
the association between a history of CM and severe COVID-19 outcomes (i.e. hospitalization or death due to COVID19), as well as COVID-19 diagnosis and vaccination as secondary outcomes. We then explored the potential mediatingroles of socio-economic status, lifestyle and pre-pandemic comorbidities, and the effect modification by polygenic
risk score for severe COVID-19 outcomes.
Results The mean age of the study population at the start of the pandemic was 67.7 (SD = 7.72) years, and 56.5%
were female. We found the number of CM types was associated with the risk of severe COVID-19 outcomesin a graded manner (pfor trend < 0.01). Compared to individuals with no history of CM, individuals exposed to any
CM were more likely to be hospitalized or die due to COVID-19 (odds ratio [OR] = 1.54 [95%CI 1.31–1.81]), particularly after physical neglect (2.04 [1.57–2.62]). Largely comparable risk patterns were observed across groups of highvs. low genetic risks for severe COVID-19 outcomes (pfor difference > 0.05). Mediation analysis revealed that 50.9%of the association between CM and severe COVID-19 outcomes was explained by suboptimal socio-economic status,
lifestyle, and pre-pandemic diagnosis of psychiatric disorders or other chronic medical conditions. In contrast, any
CM exposure was only weakly associated with COVID-19 diagnosis (1.06 [1.01–1.12]) while significantly associatedwith not being vaccinated for COVID-19 (1.21 [1.13–1.29]).
Conclusions Our results add to the growing knowledge base indicating the role of childhood maltreatment in negative health outcomes across the lifespan, including severe COVID-19-related outcomes. The identified factors underlying this association represent potential intervention targets for mitigating the harmful effects of childhood maltreatment in COVID-19 and similar future pandemics.

†

Yue Wang and Fenfen Ge contributed equally to this work.

*Correspondence:
Unnur A. Valdimarsdóttir
unnurav@hi.is
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of thislicence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Wang et al. BMC Medicine(2024) 22:319

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Keywords COVID-19, Hospitalization, Mortality, Childhood maltreatment, Psychiatric disorders

Background
COVID-19 has spread widely around the world and has
now resulted in almost 7 million deaths and 100 million
hospitalizations worldwide [1, 2]. Both death and hospitalization are commonly used indicators of the severityof COVID-19 illness [3, 4]. Accumulating evidence suggests that older age [5], male sex [5], non-White ethnicity[6], and genetic predisposition [7] are significant risk factors for severe COVID-19. In addition, psychosocial factors such as socio-economic status [8] and pre-pandemic
history of psychiatric disorders [9] have been indicatedin severe COVID-19 outcomes. While childhood maltreatment is one of the strongest risk factors for both low
socioeconomic status [10] and psychiatric disorders in
adulthood [11], less is known about its role in COVID19-related outcomes.
Childhood maltreatment, such as sexual, physical andemotional abuse, or neglect, is common worldwide, affecting 42.2% of children and adolescents in Europe and58.4% in North America [12]. Previous studies have demonstrated the role of childhood maltreatment in multiple
adverse health outcomes across the lifespan, including
psychiatric disorders, cardiovascular diseases, cancers,
and some infectious diseases [13–17]. Childhood maltreatment might impact COVID-19-related morbidity
and mortality outcomes through social, behavioural, emotional, and biological pathways [18, 19]. Indeed, individuals
exposed to childhood maltreatment have been reported tohave lower socio-economic status (educational attainment
and income) in adulthood [10], more likely to have suboptimal health behaviours (e.g. smoking) [20] and immune
function [21], and to be at higher risk of obesity [12], cardiovascular disease [22], psychiatric disorders [11], andother chronic diseases in adulthood [13], all of which areassociated with severe COVID-19 outcomes [23].
While two previous studies have reported a suggestive
link between the history of childhood maltreatment andsevere COVID-19 outcomes [24, 25], no study has yet
examined the role of specific childhood maltreatment
types on severe COVID-19 outcomes nor attempted todisentangle potential underlying mechanisms of thisassociation. Moreover, the role of genetic predisposition
to COVID-19-related morbidity and mortality [26] in theassociation between childhood maltreatment and severe
COVID-19-related outcomes remains unknown. Indeed,addressing these knowledge gaps is imperative forhealth policy and interventions targeting the reductionof potential maltreatment-related negative outcomes in
COVID-19 and similar pandemics. Therefore, leveraging

the large population-based UK Biobank cohort with prepandemic data on childhood maltreatment, we aimed tocomprehensively explore the associations between thenumber and types of childhood maltreatment and severe
COVID-19 outcomes, as well as elucidate the mechanisms underlying this association.

Methods
Study population and design

We used data from the UK Biobank cohort, which
recruited more than 500,000 participants aged
40–69 years from England, Scotland, and Wales between
2006 and 2010. At baseline, participants answered questions on demographic, socio-economic, lifestyle, andhealth-related factors and provided biological samples for genetic studies [27]. Then, 339,092 participants
who agreed to be contacted again were invited to complete online mental health questionnaires during 2016
and 2017, including a retrospective measure of childhood maltreatment. Of the invited participants, 46.4%(n = 157,366) responded to this online measure. Despite
those respondents being of higher average socio-economic status, the reported mental health problems arecomparable to the population prevalence estimates forthe corresponding age group [28].
Health-related outcomes (e.g. diagnosis, hospitalization, and death) for the participants were obtained periodically through linkage with multiple national datasets.
Specifically, hospital inpatient data were obtained from
Hospital Episode Statistics for England (from 1997 to
September 30, 2021), the Scottish Morbidity Record for
Scotland (from 1981 to July 31, 2021), and the Patient
Episode Database for Wales (from 1998 to February
28, 2018). Mortality data were obtained from National
Health Service (NHS) Digital for England as well as
Wales (from 2006 to September 30, 2021) and NHS
Central Register for Scotland (from 2006 to October 31,2021). Records of COVID-19 test results (by RT-PCR of
nose/throat swab samples) were obtained through linkage to Public Health England (i.e. PHE, from March 16,
2020, to September 30, 2021), Public Health Scotland(i.e. PHS, from March 16, 2020, to August 31, 2021), andthe Secure Anonymised Information Linkage (SAIL)
databank (from March 16, 2020, to August 31, 2021).
Additionally, information on the COVID-19 vaccination status was collected for participants of the COVID19 Self-Test Antibody study (from February 2021 to July2021). Details of the COVID-19 Self-Test Antibody study
are described elsewhere [29, 30].

Wang et al. BMC Medicine(2024) 22:319

In the present retrospective cohort study, we included
151,427 participants with information on childhood
maltreatment who were alive on January 31, 2020 (i.e.
first confirmed COVID-19 cases in the UK) in the analysis. When exploring the potential effect modification by
genetic predisposition to severe COVID-19 outcomes,
we restricted the analytic sample to only White-British
participants considering the variations in genetic predisposition across populations with different ancestries[31, 32]. We excluded participants of non-White British
ancestry (n = 14,079) or without eligible genotyping data
(i.e. genotyping rate < 99%, abnormal heterozygosity level,or kinship coefficient > 0.0884, n = 19,555) [33], leaving
117,793 participants in this analysis. Additional file 1: Fig.
S1 shows the details of the study profile.
History of childhood maltreatment

The history of childhood maltreatment was measured
using the validated Childhood Trauma Screener (CTS)[34, 35]. It consists of five items assessing whether andhow often individuals were exposed to the following
types of maltreatment during childhood: sexual abuse,physical neglect, physical abuse, emotional neglect, andemotional abuse, with response options ranging from‘0’ (never true) to ‘4’ (very often true). Weak correlations were observed among pairs of childhood maltreatment types (Additional file 1: Fig. S2). The questions andthreshold values to define each type of childhood maltreatment are based on previous published studies [22,36] and are shown in Additional file 2: Table S1. In ourstudy, we generated three types of exposure variables: (1)a binary variable indicating any childhood maltreatment,
coded as ‘0’ (no) or ‘1’ (yes); (2) a cumulative variableindicating the number of childhood maltreatment types(range from 0 to 5), which was coded as ‘0’, ‘1’, ‘2’, or ‘ ≥ 3’childhood maltreatment types according to the distribution of the entire study sample (Additional file 1: Fig. S3);and (3) five binary variables for each type of childhood
maltreatment, coded as ‘0’ (no) or ‘1’ (yes).
COVID‑19 outcomes

The main outcome of interest was severe COVID-19
outcomes during the study period (i.e. from January 31,
2020, to October 31, 2021). Specifically, participants
with a primary diagnosis (i.e. main condition treated orinvestigated) as COVID-19 (ICD-10, U07.1 or U07.2) in
hospital inpatient data or with a cause of death recorded
as COVID-19 in death registries were defined as having
severe COVID-19 outcomes. A secondary outcome ofinterest was COVID-19 diagnosis (i.e. tested positive for
COVID-19 vs. tested negative for COVID-19), which was
determined through records of COVID-19 test resultsin the PHE, PHS, and SAIL databanks from March 16,

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2020, to September 30, 2021. Another secondary outcome of interest was COVID-19 vaccination, based on
responses to the question ‘Have you received a first doseof a COVID-19 vaccine?’.
Genetic predisposition to severe COVID‑19 outcomes

We assessed the genetic predisposition to severe
COVID-19 outcomes by calculating the polygenic risk
score (PRS) for COVID-19 hospitalization or deathaccording to summary statistics (version 5) from the
COVID-19 Host Genetics Initiative large-scale GWAS
including critically ill COVID-19 patients (n = 4,792)
and the control population (n = 1,054,664) amongindividuals with European ancestry after excluding UK Biobank and 23andMe participants [32]. We
calculated the PRS using the clumping + thresholding approach [37] under 10 p-value thresholds (i.e.
5 × ­10−8, 1 × ­10−7, 1 × ­10−6, 1 × ­10−5, 1 × ­10−4, 1 × ­10−3,0.005, 0.01, 0.05, and 0.1) and validated the association
between PRS and severe COVID-19 outcomes in ourdataset by fitting logistic regression models adjustingfor birth year, sex, genotyping array, and top ten ancestry principal components. We selected the PRS withthe highest Nagelkerke R2 for further analyses (i.e. p
threshold = 5.00 × ­10−8; odds ratio = 1.21, 95% CI 1.11–
1.32; Nagelkerke R2 = 2.01%; Additional file 2: Table S2).
To avoid inflated type I error from overfitting, we additionally performed a principal component (PC) analysis
on the set of the 10 PRSs and used the first PRS-PC for
sensitivity analyses [38]. In our dataset, the first PRSPC for severe COVID-19 outcomes showed a strong
association with the severe COVID-19 outcome phenotype (odds ratio = 1.22, 95% CI 1.11–1.33; Nagelkerke
R2 = 2.01%). More information about the PRS-PC analysis is shown in Additional file 1: Fig. S4.
Covariates

We considered birth year (< 1950, 1950–1959 or ≥ 1960),
sex (female or male), ethnicity (White [British, Irish,
and any other White background], non-White [mixed,
Asian or Asian British, Black or Black British, Chinese,and others], or unknown) and recruitment region (Scotland, England, or Wales) as potential confounders due
to previously reported associations to both primary
exposure [39, 40] and outcome [5, 6, 41] (Fig. 1). Also,
based on previous findings [10–13, 20, 23], we selectedfour variable clusters as potential mediators: (1) socioeconomic status (i.e. Townsend deprivation index [TDI,
lower than median, higher than median, or unknown],
annual household income [≤ £18,000, £18,000–£30,999,£31,000–£51,999, £52,000–£100,000, ≥ £100,000, or
unknown], and college education [no, yes, or unknown]);

Wang et al. BMC Medicine(2024) 22:319

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Fig. 1 Proposed causal model with alternative pathways of how childhood maltreatment could influence severe COVID-19 outcomes (i.e.
hospitalization or death due to COVID-19), taking into account the availability of data(2) lifestyle factors (i.e. smoking status [never, previous,
current, or unknown] and body mass index [BMI, < 25 kg/
m2, 25–29.9 kg/m2, ≥ 30 kg/m2, or unknown]); (3) prepandemic chronic medical conditions (no or yes); and (4)pre-pandemic psychiatric disorders (no or yes).
Specifically, TDI was calculated based on the postcodeof participants’ address, representing the deprivation at
a population level [42]. BMI was calculated using kilogrammes (kg) divided by the square of height in metres­(m2) using anthropometric data measured at the assessment centre at baseline. We calculated the Charlson
Comorbidity Index (CCI) based on Deyo’s coding algorithm [43] using hospital inpatient data (before January
31, 2020), and patients with a CCI ≥ 1 were considered
to have pre-pandemic chronic medical conditions. Additional file 2: Table S3 provides more details about the diseases included in the calculation of the CCI. We defined
pre-pandemic psychiatric disorders as any diagnosis of
psychiatric disorders (ICD-10, F10–F99) in hospital inpatient data before January 31, 2020.
Statistical analysis

We first compared the characteristics of participants by
the history of childhood maltreatment using ANOVAs
for continuous data and chi-square tests for categorical
data. We then performed binomial logistic regression
to estimate the association between a history of childhood maltreatment and severe COVID-19 outcomes, aswell as COVID-19 diagnosis and COVID-19 vaccination(i.e. secondary outcomes), with the estimates reported
as odds ratios (ORs) and 95% confidence intervals (CIs).
The basic model (model 1) was adjusted for demographic
factors (i.e. birth year, sex, ethnicity, and recruitment
region). In a stepwise approach, the four variable clusters of mediators were additionally adjusted to examine
whether and to what extent the ORs between childhood
maltreatment and severe COVID-19 outcomes wereattenuated (models 2–5). We then conducted a regression-based causal mediation analysis using the CMAverse
package in R [44–46] to estimate the proportion of mediation effect by the four variable clusters of mediators
individually (M1–M4) and combined (M5). Specifically,
the outcomes were regressed by the primary exposure
variable (i.e. any childhood maltreatment), specific variable cluster of mediators, and demographic factors in abinomial logistic regression model. Each mediator wasthen regressed by exposure and demographic factors ineither binomial (e.g. pre-pandemic psychiatric disorders)or multinomial (e.g. BMI) logistic regression models. The
results of the outcome and mediator models were then
combined to calculate the proportion of mediation.
To determine the association of specific types of childhood maltreatment with severe COVID-19 outcomes,
we ran separate analyses for each of the five childhood
maltreatment types. Furthermore, to examine the potential effect modification by genetic predisposition, weWang et al. BMC Medicine(2024) 22:319

stratified our main analyses of the association between
childhood maltreatment (both as a binary variable and acumulative variable) and severe COVID-19 by tertile of
the PRS or the first PRS-PC for severe COVID-19 outcomes (i.e. low, < 1st tertile; intermediate, 1st to 2nd
tertile; high, > 2nd tertile). The differences between thegroups were tested by introducing interaction terms (i.e.
childhood maltreatment × PRS for severe COVID-19 outcomes) in the logistic regression adjusted for birth year,
sex, ethnicity, and recruitment region. We then obtained
p-values to indicate the statistical significance of theinteraction terms through the Wald test.
In sensitivity analyses, we first restricted the analysis of the association between childhood maltreatment
and severe COVID-19 outcomes to individuals with
COVID-19 diagnosis—a population effectively at risk ofsevere COVID-19-related outcomes. Then, to address
the potential impact of COVID-19 vaccination, whichstarted on December 8, 2020, in the UK [47], we reran
the main analysis by redefining the study period fromJanuary 31, 2020, to December 8, 2020 (i.e. before vaccination roll out). Additionally, given the difference in data
coverage across registries (e.g. hospital inpatient data anddeath registries) and recruitment regions (i.e. England,
Scotland, and Wales), we repeated the main analysis byexcluding participants registered in Wales as well as byredefining the study period from January 31, 2020, to July
31, 2021. Moreover, instead of using cut-off scores forthe measure of childhood maltreatment, we repeated ourmain analysis using the total CTS score (ranging from 0
to 20), to capture the full range of variability in the severity of childhood maltreatment. Finally, as our primary
outcome was hospitalization or death due to COVID-19
as a combined indicator of severe COVID-19, we performed a sensitivity analysis for hospitalization and death
due to COVID-19, separately, to determine if there wereany differences between these two outcomes.
The regression function for each analysis was shown in
the Additional file 3. All analyses were completed using R(version 4.0) and Plink (version 1.9), and a two-tailed testwith p < 0.05 was considered statistically significant.

Results
Of 151,427 participants included in the present study,
56.5% were female, and the mean (SD) age at the startof the pandemic was 67.7 (7.72) years. Nearly one-third(n = 50,441) of the participants reported at least one
type of childhood maltreatment, and emotional neglect(22.2%) was the most commonly reported type, while
physical neglect (5.6%) was the least commonly reported
type (Additional file 2: Table S1). Compared with unexposed individuals, those who were exposed to childhood
maltreatment tended to have a lower level of education

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and annual household income (p < 0.001). They were also
more likely to be younger, female, non-White ethnicity,
recruited from England, have high BMI (i.e. ≥ 30 kg/m2),
and with pre-pandemic chronic medical conditions aswell as psychiatric disorders (p < 0.001; Table 1).
A total of 606 individuals were hospitalized (n = 542)
and/or died (n = 155) as a result of COVID-19 during
the study period. We observed increased odds of severe
COVID-19 outcomes among patients exposed to any
childhood maltreatment (OR = 1.54 [95% CI 1.31–1.81];
Table 2) when compared with unexposed individuals inthe basic model (model 1). The association was amplified in a graded manner by the cumulative number ofchildhood maltreatment types (pfor trend < 0.01). Specifically, those who experienced three or more childhood maltreatment types had the highest odds of severe
COVID-19 outcomes (2.32 [1.73–3.05]), followed bythose who experienced two (1.62 [1.22–2.10]) or one(1.33 [1.09–1.62]) type. The inclusion of potential mediators in the models attenuated the magnitude of the association, although ORs remained statistically significantly
higher than one in the fully adjusted model (model 5)among individuals with any childhood maltreatment(1.26 [1.07–1.48]) and those who experienced three ormore types of childhood maltreatment (1.50 [1.11–1.98]).
Of the five types of childhood maltreatment, physical neglect yielded the strongest association with severe
COVID-19 outcomes in the basic model (model 1, 2.04
[1.57–2.62]; Fig. 2) as well as in the model adjusted for all
variable clusters of mediators (model 5, 1.52 [1.16–1.96]),
although the differences between the groups were notstatistically significant (pfor difference > 0.05).
The majority of the association between childhood maltreatment and severe COVID-19 outcomes was mediatedthrough lifestyle factors (27.8%; Fig. 3), followed by socioeconomic factors (20.5%), pre-pandemic chronic medical conditions (17.4%), and psychiatric disorders (16.6%).
In total, 50.9% of the association was mediated by all
studied mediators and ranged from 49.5% (after physical neglect) to 79.0% (after sexual abuse) across different
types of childhood maltreatment.
We obtained largely comparable results when stratified by tertiles of PRS for severe COVID-19 outcomes(pfor difference > 0.05; Fig. 4). Specifically, exposure to any
childhood maltreatment (low genetic risk, 1.88 [1.30–2.69];
intermediate genetic risk, 1.41 [1.01–1.95]; high genetic
risk, 1.55 [1.14–2.09]) and three or more types of childhood maltreatment (low genetic risk, 2.68 [1.29–5.00];
intermediate genetic risk, 2.14 [1.11–3.78]; high genetic
risk, 3.11 [1.84–4.98]) were both consistently associatedwith significantly increased odds of severe COVID-19
outcomes, regardless of PRS for severe COVID-19 outcomes. We observed similar results when stratifying by

Wang et al. BMC Medicine(2024) 22:319

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Table 1 Characteristics of the study population
History of childhood maltreatment
No (n = 100,986)

Overall (n = 151,427)
Yes (n = 50,441)

Age at the measure of childhood maltreatment
Mean (SD)64.1 (7.69)

63.3 (7.75)

63.8 (7.72)

Median [min, max]

65.0 [46.0, 81.0]

64.0 [46.0, 80.0]

65.0 [46.0, 81.0]

Age at the start of the pandemic (i.e. 2020)
Mean (SD)

68.0 (7.69)

67.2 (7.75)

67.7 (7.72)

Median [min, max]

69.0 [50.0, 84.0]

68.0 [50.0, 84.0]

69.0 [50.0, 84.0]

Birth year
< 195044,042 (43.6%)

19,711 (39.1%)

63,753 (42.1%)

1950–1959

36,316 (36.0%)

18,997 (37.7%)

55,313 (36.5%)≥ 196020,628 (20.4%)

11,733 (23.3%)

32,361 (21.4%)

Sex
Female55,882 (55.3%)

29,709 (58.9%)

85,591 (56.5%)

Male45,104 (44.7%)

20,732 (41.1%)

65,836 (43.5%)

EthnicityWhite

98,688 (97.7%)

47,975 (95.1%)

146,663 (96.9%)  British

93,211 (92.3%)

44,137 (87.5%)

137,348 (90.7%)  Irish2044 (2.0%)1523 (3.0%)3567 (2.4%)  Others

3433 (3.4%)2315 (4.6%)

5748 (3.8%)

Non-White  Mixed2013 (2.0%)2250 (4.5%)4263 (2.8%)356 (0.4%)428 (0.8%)784 (0.5%)   Asian or Asian British

650 (0.6%)595 (1.2%)

1245 (0.8%)   Black or Black British

451 (0.4%)609 (1.2%)

1060 (0.7%)  Chinese

144 (0.1%)

203 (0.4%)347 (0.2%)  Others

412 (0.4%)415 (0.8%)827 (0.5%)

Unknown

285 (0.3%)

216 (0.4%)501 (0.3%)

Recruitment region
Scotland

7019 (7.0%)

3137 (6.2%)

10,156 (6.7%)

Wales3771 (3.7%)

1795 (3.6%)

5566 (3.7%)

England90,196 (89.3%)

45,509 (90.2%)

135,705 (89.6%)

Townsend deprivation index
Lower than median (< − 2.43)

52,826 (52.3%)

22,825 (45.3%)

75,651 (50.0%)

Higher than median (≥ − 2.43)

48,041 (47.6%)

27,540 (54.6%)

75,581 (49.9%)

Unknown

119 (0.1%)

76 (0.2%)

195 (0.1%)

College education
No

46,757 (46.3%)

24,230 (48.0%)

70,987 (46.9%)

Yes

47,047 (46.6%)

21,806 (43.2%)

68,853 (45.5%)

Unknown

7182 (7.1%)

4405 (8.7%)

11,587 (7.7%)

Annual household income≤ £18,000

10,924 (10.8%)7478 (14.8%)

18,402 (12.2%)£18,000–£30,999

20,838 (20.6%)

10,886 (21.6%)

31,724 (21.0%)£31,000–£51,999

26,403 (26.1%)

13,223 (26.2%)

39,626 (26.2%)£52,000-£100,000

24,926 (24.7%)

10,946 (21.7%)

35,872 (23.7%)≥ £100,000

7812 (7.7%)3195 (6.3%)

11,007 (7.3%)

Unknown

10,083 (10.0%)

4713 (9.3%)

14,796 (9.8%)

Smoking status
Never

61,041 (60.4%)

26,272 (52.1%)

87,313 (57.7%)

Previous

33,519 (33.2%)

19,440 (38.5%)

52,959 (35.0%)

Current

6215 (6.2%)

4589 (9.1%)

10,804 (7.1%)

Wang et al. BMC Medicine(2024) 22:319

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Table 1 (continued)
History of childhood maltreatment

Unknown

Overall (n = 151,427)

No (n = 100,986)

Yes (n = 50,441)

211 (0.2%)

140 (0.3%)351 (0.2%)

Body mass index, kg/m2 a< 25

40,352 (40.0%)

18,488 (36.7%)

58,840 (38.9%)

25–29.9

41,925 (41.5%)

20,621 (40.9%)

62,546 (41.3%)≥ 30

18,477 (18.3%)

11,191 (22.2%)

29,668 (19.6%)

Unknown

232 (0.2%)

141 (0.3%)373 (0.2%)

Pre-pandemic chronic medical conditions b
No73,538 (72.8%)

35,050 (69.5%)

108,588 (71.7%)

Yes

27,448 (27.2%)

15,391 (30.5%)

42,839 (28.3%)

Pre-pandemic psychiatric disordersNo

94,307 (93.4%)

44,493 (88.2%)

138,800 (91.7%)

Yes6679 (6.6%)5948 (11.8%)

12,627 (8.3%)

a
The body mass index was calculated using weight kilogrammes (kg) by the square of height in metres (­ m2), using anthropometric data measured at the assessment
centre at baselineb

We calculated the Charlson Comorbidity Index using hospital inpatient data (before January 31, 2020), and patients with a CCI ≥ 1 were considered to have prepandemic chronic medical conditions

Table 2 Association between history of childhood maltreatment and COVID-19 outcomes (OR and 95% CI)
Case/N (%)

Model 1aModel 2bModel 3c

Model 4dModel 5e

Outcome: severe COVID-19 outcomes (i.e. hospitalization or death due to COVID-19)
Exposure: any childhood maltreatment  No345/100,986 (0.34)

Ref

Ref

Ref

Ref

Ref

  Yes

261/50,441 (0.52)

1.54 (1.31–1.81)

1.42 (1.21–1.68)

1.33 (1.12–1.56)

1.28 (1.08–1.51)

1.26 (1.07–1.48)

Exposure: number of childhood maltreatment types  0345/100,986 (0.34)

Ref

Ref

Ref

Ref

Ref

  1

141/30,819 (0.46)

1.33 (1.09–1.62)

1.27 (1.04–1.54)

1.22 (1.00–1.48)

1.20 (0.98–1.46)

1.19 (0.97–1.44)  2

62/11,586 (0.54)

1.62 (1.22–2.10)

1.47 (1.11–1.91)

1.34 (1.01–1.75)

1.29 (0.97–1.68)

1.27 (0.95–1.65)  ≥ 358/8036 (0.72)

2.32 (1.73–3.05)

2.00 (1.49–2.63)

1.69 (1.26–2.24)

1.56 (1.16–2.06)

1.50 (1.11–1.98)

Outcome: COVID-19 diagnosisf
Exposure: any childhood maltreatment  No5362/35,050 (15.30)

Ref

Ref

Ref

Ref

Ref

  Yes

2994/18,028 (16.61)

1.06 (1.01–1.12)

1.02 (0.97–1.08)

1.01 (0.96–1.07)

1.02 (0.97–1.07)

1.02 (0.97–1.07)

Ref

Ref

Ref

Ref

Exposure: number of childhood maltreatment types  05362/35,050 (15.30)

Ref

  1

1735/10,873 (15.96)

1.03 (0.97–1.10)

1.01 (0.95–1.07)

1.00 (0.94–1.06)

1.00 (0.94–1.07)

1.01 (0.95–1.07)  2

714/4209 (16.96)

1.08 (0.99–1.18)

1.04 (0.95–1.13)

1.02 (0.94–1.12)

1.03 (0.94–1.12)

1.03 (0.94–1.13)  ≥ 3

545/2946 (18.50)

1.14 (1.03–1.26)

1.07 (0.97–1.18)

1.05 (0.94–1.16)

1.05 (0.95–1.17)

1.06 (0.96–1.18)

aModel 1: adjusted for demographic factors (birth year, sex, ethnicity, and recruitment region)b

Model 2: model 1 additionally adjusted for socio-economic status (Townsend deprivation index, college education, and annual household income)cModel 3: model 2 additionally adjusted for lifestyle-related factors (smoking status and body mass index)dModel 4: model 3 additionally adjusted for pre-pandemic chronic medical conditions (Charlson Comorbidity Index ≥ 1, before January 31, 2020)eModel 5: model 4 additionally adjusted for pre-pandemic psychiatric disorders (ICD-10, F10–F99; before January 31, 2020)

f
COVID-19 diagnosis was determined through records of positive COVID-19 test results in the PHE, PHS, and SAIL databanks (n = 8356) and compared with individuals
who had records of negative COVID-19 test results (n = 44,722)

Wang et al. BMC Medicine(2024) 22:319

Page 8 of 13

Finally, in the secondary analyses, we found any
childhood maltreatment and the number of childhood
maltreatment types were both consistently associatedwith significantly increased odds of being unvaccinatedfor COVID-19 (models 1–5; Additional file 2: Table S9).
By contrast, we found a weak association between any
childhood maltreatment (model 1, 1.06 [1.01–1.12])
and three or more types of childhood maltreatment(model 1, 1.14 [1.03–1.26]) with COVID-19 diagnosis,
which attenuated to null when adding potential mediators to the model (models 2–5; Table 2).

Fig. 2 Association between history of childhood maltreatment(CM) and severe COVID-19 outcomes (i.e. hospitalization or death
due to COVID-19) by types of childhood maltreatment. Note: model
1—adjusted for demographic factors (birth year, sex, ethnicity,and recruitment region); model 2—model 1 additionally adjusted
for socio-economic factors (Townsend deprivation index, college
education, and annual household income); model 3—model 2
additionally adjusted for lifestyle-related factors (smoking status
and body mass index); model 4—model 3 additionally adjusted
for pre-pandemic chronic medical conditions (Charlson Comorbidity
Index ≥ 1, before January 31, 2020); model 5—model 4 additionallyadjusted for pre-pandemic psychiatric disorders (ICD-10, F10–F99;
before January 31, 2020)

the first PRS-PC for severe COVID-19 outcomes (Additional file 1: Fig. S5).
In the sensitivity analyses, we obtained largely comparable results when restricting our analysis to individuals with a COVID-19 diagnosis (Additional file 2:
Table S4), redefining the study period before vaccination rollout (Additional file 2: Table S5), and excluding participants registered in Wales and redefining thestudy period from January 31, 2020, to July 31, 2021
(Additional file 2: Table S6). Also, we observed similar
result patterns when using the total CTS score (Additional file 2: Table S7) or analysing hospitalization anddeath due to COVID-19 as separate outcomes (Additional file 2: Table S8).

Discussion
The findings of this cohort study with pre-pandemic
data on childhood maltreatment suggest a robust dose–
response association between the number of childhood
maltreatment types and severe COVID-19 outcomes.
While all types of childhood maltreatment were associated with severe COVID-19 outcomes, physical neglect
in childhood yielded the strongest association. The
associations were partly mediated by suboptimal socioeconomic status, lifestyle, and pre-pandemic psychiatricdisorders or other chronic medical conditions and were
not modified by genetic predisposition to severe COVID19 outcomes.
In line with the findings of two previous studies [24,25], our findings confirm the association between childhood maltreatment and severe COVID-19 outcomes.
Our findings moreover extend the current level of evidence by showing that all types of childhood maltreatment, ranging from sexual abuse to physical neglect, arerobustly associated with severe COVID-19 outcomes. In
our study, physical neglect in childhood yielded the highest odds ratios of severe COVID-19-related outcomes,
which is similar to the findings from previous studies
on other long-term physical outcomes after childhood
physical neglect, including test-identified sexually transmitted infections, diabetes, and lung disease [48, 49].
Although the mechanisms underlying this finding remain
unclear, it is possible that individuals who experience
physical neglect may not receive necessary medical carein childhood, leading to a lack of awareness or appropriate utilization of medical services in adulthood, thereby
contributing to the increased risk of severe health consequences in adulthood [50, 51].
Importantly, our findings indicate that more than halfof the association between childhood maltreatment andsevere COVID-19 outcomes is mediated by suboptimalsocio-economic status, lifestyle, and comorbid psychiatric or other chronic medical conditions. These results are
consistent with previous findings suggesting that childhood maltreatment may increase the risk of health problems in adulthood through multiple factors, including

Wang et al. BMC Medicine(2024) 22:319

Page 9 of 13

Fig. 3 Mediating roles of socioeconomic status, lifestyle, and pre-pandemic chronic medical conditions or psychiatric disorders on the associations
between history of childhood maltreatment and severe COVID-19 outcomes (i.e. hospitalization or death due to COVID-19). Note: M0—adjusted
for demographic factors (birth year, sex, ethnicity, and recruitment region); M1—M0 and additionally adjusted for socio-economic status (Townsend
deprivation index, college education, and annual household income); M2—M0 additionally adjusted for lifestyle-related factors (smoking status
and body mass index); M3—M0 additionally adjusted for pre-pandemic chronic medical conditions (Charlson Comorbidity Index ≥ 1, before January
31, 2020); M4—M0 additionally adjusted for pre-pandemic psychiatric disorders (ICD-10, F10–F99; before January 31, 2020); M5—M0 additionallyadjusted for socioeconomic status, lifestyle, and pre-pandemic chronic medical conditions psychiatric disorders; proportion of mediation:
the proportion of the total effect that is mediated through the specified mediators

the adoption of adverse health behaviours and increased
vulnerabilities to obesity and other chronic medical conditions of relevance for COVID-19 severity [13, 20, 23].
In line with previous findings [52, 53], we found thatchildhood maltreatment survivors were more likely to
be unvaccinated against COVID-19 which may result ingreater risks of severe COVID-19 outcomes [54]. Yet, inthe present study, most of the severe COVID-19 outcome
events occurred before the introduction of the COVID19 vaccine, and therefore, we observed similar estimateswhen redefining the study period before the vaccination
rollout.
We further found the history of psychiatric disorders
to mediate the association between childhood maltreatment and severe COVID-19 outcomes. Indeed, there
is strong evidence for the associations between childhood maltreatment and the risk of psychiatric disorders[11], coupled with our [9] and more recent findings [55]indicating a role of pre-existing psychiatric disorders insevere COVID-19 outcomes. Among the four studied
variable clusters of mediators, lifestyle-related factors
appear to have the strongest contribution to the association between childhood maltreatment and severe
COVID-19 outcomes. However, there is an establishedlink between lifestyle factors and socio-economic status [56], as well as multiple diseases, including cardiometabolic conditions [57] and mental disorders [58].
Therefore, the proportion mediated by each cluster of

mediators, as suggested in the causal mediation analysis,
is likely confounded by the other mediating clusters.
We found that the association between childhood maltreatment and severe COVID-19 outcomes remainedrobust after controlling for these potential mediators aswell as genetic susceptibility to severe COVID-19 outcomes. Therefore, other unmeasured biological pathways, including disruption of inflammatory responses[59] and hormonal dysregulation [19], may contributeto the elevated risk of severe COVID-19 outcomes. For
instance, childhood maltreatment has been associatedwith immune dysregulation [60, 61], such as disruption
in immune cell activation [62], increased proinflammatory cytokine production [63], and accelerated telomere
erosion [64], which may reduce an individual’s capacity to recover from COVID-19. Indeed, recent evidence
suggests that elevated IL-6 and TNF-α levels can predict
disease severity and survival in patients with COVID19 [65]. In addition, previous studies report an atypicalhypothalamic–pituitary–adrenal axis stress response
among childhood maltreatment survivors [66], which has
been identified as a potential risk factor of severe illness
in COVID-19 [67]. In contrast, increased susceptibility
to COVID-19 infection is an unlikely explanation for theelevated risk of severe COVID-19-related outcomes bychildhood maltreatment, as we found weak or no associations between childhood maltreatment and COVID-19
diagnosis.

Wang et al. BMC Medicine(2024) 22:319

Fig. 4 Association between history of childhood maltreatment(CM) and severe COVID-19 outcomes (i.e. hospitalization or death
due to COVID-19) by levels of polygenic risk score (PRS) to severe
COVID-19 outcomes. *Adjusted for demographic factors (birth year,
sex, ethnicity, and recruitment region)

Strengths and limitations of this study

The major strength of our study is the use of a longitudinal study design, i.e. pre-pandemic individual data onchildhood maltreatment and follow-up data on COVID19, in a large population-based cohort. This ensuresthat the measures of childhood maltreatment indeed
preceded any severe COVID-19 outcomes and hence
minimizes the risk of reverse causality. Additionally, theprimary outcome of interest was death or hospitalization
with COVID-19 as the primary diagnosis, as opposed toalso including secondary diagnoses in a previous study[25], reducing risks of misclassification of the outcome.
Also, by utilizing severe COVID-19 events as the outcome, the influence of surveillance bias should be minor.
Moreover, our consideration of genetic predisposition tosevere COVID-19 outcomes and a wide range of mediators provides evidence of pathways linking childhood
adversities to severe COVID-19 outcomes, with potential
relevance for prevention and intervention strategies.

Page 10 of 13

This study also has several limitations to be noted.
First, as in most studies on childhood maltreatment,
information on childhood maltreatment was recalled byparticipants in middle or older age rather than captured
prospectively (in childhood), which may be liable tounderreport [28] and biased by current mental state [68].
However, to explain the observed result pattern, suchmeasurement error would have to be systematic in relation to later severe COVID-19 outcomes. Second, we do
not have information on childhood poverty or parentalsocio-economic status, and several included mediators(e.g. smoking status, BMI) were only measured once atbaseline and might have changed over the 10-year follow-up. Third, the incidence of COVID-19 varied acrosspopulations and geographical regions [41], yet our sensitivity analyses restricted to individuals with a COVID19 diagnosis, excluding participants registered in Walesand confined to the study period from January 31, 2020,
to July 31, 2021, suggested a minimal influence of thesefactors on the reported associations. Fourth, the identification of COVID-19 cases relies solely on RT-PCR testing which may lead to underestimation of the COVID-19
diagnosis. Also, the identified hospitalization or mortality
rate in our study is lower than the reported rate in the UK
during the same period [2]. Indeed, there is evidence of a‘healthy volunteer’ selection bias of the UK participants
who were more likely to live in less socioeconomicallydeprived areas and have lower rates of all-cause mortality [69]. In addition, most severe childhood maltreatment
cases were probably not included in the cohort, possibly
resulting in an underestimation of the studied association. Finally, the UK Biobank cohort is not representativeof the entire UK population [69], and only approximately
30% of the UK Biobank participants were included in our
analysis; thus, the generalization of our findings should
be made with caution.

Conclusions
Our findings suggest that a history of childhood maltreatment, including exposure to physical and emotional neglect or abuse, is robustly associated with severe
COVID-19 outcomes. This association was not modified
by genetic predisposition to severe COVID-19 outcomesbut partly mediated by suboptimal socio-economic status, lifestyle factors, and comorbidities. The latter constitute potential targets for clinical and public healthinterventions. These findings highlight the role of earlylife adversities in severe health consequences across thelifespan and call for increased clinical surveillance ofpeople exposed to childhood maltreatment in COVID-19
outbreaks and future pandemics.

Wang et al. BMC Medicine(2024) 22:319

Page 11 of 13

Abbreviations
BMI	Body mass index
CCI	Charlson Comorbidity Index
CM	Childhood maltreatment
CTS	Childhood Trauma Screener
NHS	National Health Service
PC	Principal component
PHE	Public Health EnglandPHS	Public Health Scotland
PRS	Polygenic risk score
SAIL	Secure Anonymised Information Linkage
TDI	Townsend deprivation index

Funding
This work was supported by NordForsk (NO. 105668/138929 to UAV) and a
Horizon2020 grant (No. 847776 to OAA, FF, and UAV). HA was supported by
the Research Council of Norway (RCN, #324620) and NordForsk (NO. 156298).
OAA was supported by RCN (#223273, #324499). HZ was supported by an
UNSW Scientia Program Award. KL was supported by the Estonian ResearchCouncil (NO. PSG615).

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03399-8.
Additional file 1: Fig. S1. Study profile. Fig. S2. Spearman rank correlation matrix for different types of childhood maltreatment. Fig. S3. Distribution of cumulative number of childhood maltreatment types. Fig. S4.
Principal component on the set of the 10 polygenic risk score for severe
COVID-19 outcomes. Fig. S5. Association between history of childhood
maltreatment and severe COVID-19 outcomes, by levels of first PRS-PC tosevere COVID-19 outcomes.
Additional file 2: Table S1. Participants’ response to the 5 types of childhood maltreatment. Table S2. Associations between polygenic risk scoresfor severe COVID-19 outcomes. Table S3. Diseases used for calculating
Charlson comorbidity index. Table S4. Association between history ofchildhood maltreatment and severe COVID-19 outcomes, restricted analysis to individuals with COVID-19 diagnosis. Table S5. Association between
history of childhood maltreatment and severe COVID-19 outcomes,
re-defining the study period before vaccination roll out. Table S6. Association between history of childhood maltreatment and severe COVID-19
outcomes, excluding participants registered in Wales as well as re-defining
the study period from January 31st, 2020 to July 31st, 2021. Table S7.
Association between total childhood trauma screener score and COVID-19
outcomes. Table S8. Association between any childhood maltreatment
and severe COVID-19 outcomes, separating for hospitalization and death
due to COVID-19. Table S9. Association between history of childhood
maltreatment and COVID-19 vaccination.
Additional file 3. Regression function for each analysis.
Acknowledgements
This research was conducted using the UK Biobank Resource under application number 76517. This work uses data provided by patients and collected by
the NHS as part of their care and support. This research used data assets madeavailable by National Safe Haven as part of the Data and Connectivity NationalCore Study, led by Health Data Research UK in partnership with the Officefor National Statistics and funded by UK Research and Innovation (grant ref
MC PC 20058). The computations and data handling were partly enabled byresources provided by the National Academic Infrastructure for Supercomputing in Sweden (NAISS) and the Swedish National Infrastructure for Computing (SNIC) at Uppsala Multidisciplinary Centre for Advanced Computational
Science (UPPMAX) partially funded by the Swedish Research Council throughgrant agreements no. 2022-06725 and no. 2018-05973.
Authors’ contributions
YW and UAV designed the study. YW performed the phenotypic analysis and FG performed the genotypic analysis, with supervision from UAV, TA,
KH, and FF. YW, FG, and UAV drafted the manuscript. HA, AH, JJ, HZ, QS, HCW,
OBVP, KL, OAA, FF, and HS provided critical feedback. All authors contributed
to the data interpretation. All authors read and approved the final manuscript.
Authors’ Twitter handles
Twitter handles: @Yue_surpassyue (Yue Wang); @Fenfen101 (Fenfen Ge); @UnnurAValdimars (Unnur A Valdimarsdóttir); @thoraspelund (Thor Aspelund);@helga_ask (Helga Ask); @lehto_kelli (Kelli Lehto); @HelgaZoega (Helga
Zoega); @oleaandreassen (Ole A Andreassen)

Availability of data and materials
Data from the UK Biobank (http://​www.​ukbio​bank.​ac.​uk/) are available to all
researchers upon making an application.

Declarations
Ethics approval and consent to participate
This study was approved by the ethics review authority (2022–01516-01)
in Sweden. The UK Biobank has approval from the North West Multi-Centre
Research Ethics Committee as a Research Tissue Bank approval (11/NW/0382).
Consent for publication
Not applicable.
Competing interests
OAA is a consultant to cortechs.ai and received a speaker’s honorarium from
Lundbeck, Sunovion, and Janssen. All other authors declare that they have nocompeting interests.
Author details
1
Centre of Public Health Sciences, Faculty of Medicine, University of Iceland,
Reykjavík, Iceland. 2 Institute of Environmental Medicine, Karolinska Institutet,
Stockholm, Sweden. 3 PsychGen Centre for Genetic Epidemiology and Mental
Health, Norwegian Institute of Public Health, Oslo, Norway. 4 Departmentof Psychology, University of Oslo, Oslo, Norway. 5 School of Population Health,
Faculty of Medicine and Health, UNSW Sydney, Sydney, Australia. 6 ClinicalResearch Center for Mental Disorders, Shanghai Pudong New Area Mental
Health Center, Tongji University School of Medicine, Shanghai, China. 7 Institutefor Advanced Study, Tongji University, Shanghai, China. 8 Centre for ClinicalBrain Sciences, University of Edinburgh, Edinburgh, UK. 9 Generation Scotland,Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK.
10
Department of Clinical Medicine, Faculty of Health and Medical Sciences,
University of Copenhagen, Copenhagen, Denmark. 11 Department of Clinical Immunology, Zealand University Hospital, Roskilde, Denmark. 12 Estonian
Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia.
13
Institute of Clinical Medicine, NORMENT Centre, University of Oslo, Oslo,
Norway. 14 Division of Mental Health and Addiction, NORMENT Centre, Oslo
University Hospital, Oslo, Norway. 15 West China Biomedical Big Data Center,
West China Hospital, Sichuan University, Chengdu, Sichuan, China. 16 Med‑X
Center for Informatics, Sichuan University, Chengdu, Sichuan, China. 17 Department of Epidemiology, Harvard T H Chan School of Public Health, Boston, MA,
USA.
Received: 3 October 2023 Accepted: 22 April 2024

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